You are working with a Snowpark DataFrame named containing information about products, including 'CATEGORY , 'SUBCATEGORY , and 'PRICE'. You want to determine the maximum price for each subcategory within each category. Furthermore, you need to filter the results to only include categories that have more than 5 subcategories. Which of the following Snowpark Python code snippets accomplishes this task? (Select all that apply)

• A.
  
• B.
  
• C.
  
• D.
  
• E.
  

You're working with Snowpark and want to load data from a Pandas DataFrame into a Snowpark DataFrame. The Pandas DataFrame, 'customer_data' , contains columns with mixed data types (integers, strings, dates). Some columns also contain NULL values. You need to ensure that the data types are correctly inferred by Snowpark, NULL values are handled appropriately, and the resulting Snowpark DataFrame 'snowpark_customers' can be used for further transformations. What is the best approach to achieve this with minimal code and maximum performance?

• A. Explicitly define the schema with StructType and StructField, specifying the column names and data types based on the Pandas DataFrame, converting null values to Snowflake's null representation during DataFrame creation.
• B. Infer the schema explicitly before creating the Snowpark DataFrame using Pandas DataFrame column types. For the string columns, define them to be StringType().
• C. First, replace all NA/NaN values in Pandas DataFrame with None, then create Snowpark DataFrame using 'session.createDataFrame(customer_datay.
• D. Use 'session.write_pandas' because its optimized for large pandas dataframe.
• E. Use 'session.createDataFrame(customer_datay and rely on Snowpark to automatically infer the schema and handle NULL values implicitly. Convert any problematic columns after the Snowpark DataFrame is created.

You are developing a Snowpark application to load data into a Snowflake table named 'SALES DATA. The DataFrame 'sales_df contains new sales records. You need to insert these records into 'SALES DATA. Which of the following Snowpark DataFrame methods will efficiently perform this operation, considering potential data type mismatches between the DataFrame and the target table? Assume no explicit schema definition is necessary.

• A. Option D
• B. Option A
• C. Option C
• D. Option B
• E. Option E

A Snowpark application needs to process large volumes of sensor data stored in a Snowflake table named , which includes columns , 'timestamp' , and The application must calculate a rolling average of for each over a 5-minute window. The data is not perfectly ordered by 'timestamp' within each 'sensor_id'. What is the MOST efficient and accurate way to implement this rolling average calculation using Snowpark?

• A. Implementing a Python UDTF (User-Defined Table Function) that iterates through the data for each calculates the rolling average manually, and emits the results as rows.
• B. Using a Window specification with 0)' and the 'avg()' window function. (Where 'to_seconds' converts a duration to seconds)
• C. Using a Window specification with 'orderBy('timestamp')' and 'rowsBetween(Window.unboundedPreceding, Window.currentRow)' to calculate the cumulative average, then subtracting the average from 5 minutes ago. The query will then be grouped on the sensor id.
• D. Using after applying a filter to select only the data within the 5-minute window, updating the filter for each new window.
• E. Using a Window specification with 'orderBy('timestamp')' and 'rowsBetween(Window.unboundedPreceding, Window.currentRow)' in conjunction with and a UDF to manually calculate the rolling average within each group.

You have a Snowpark application that reads data from a large Snowflake table and performs several transformations. During testing, you observe that the application's performance is inconsistent, with some runs taking significantly longer than others, even with the same input data'. You suspect that data locality might be a contributing factor. What steps can you take within your Snowpark application to investigate and potentially improve data locality and performance consistency?

• A. Ensure the Snowpark session is configured with a large enough warehouse size to minimize data spilling to disk.
• B. Enable Snowflake's automatic clustering on the underlying table if it's not already enabled. This will physically organize the data on disk based on the clustering key.
• C. Use to redistribute the data across the cluster based on a relevant key. This can improve data locality for subsequent operations.
• D. Disable Snowflake's result cache. This ensures that the application always reads the most recent data from disk, regardless of performance impact.
• E. Implement caching using , combined with a targeted 'repartition()' to ensure that frequently accessed data is readily available in memory close to the processing nodes.